Radial Turboexpander Optimization Over Discretized Heavy-Duty Test Cycles for Mobile Organic Rankine Cycle Applications

Author(s):  
Miles C. Robertson ◽  
Aaron W. Costall ◽  
Peter J. Newton ◽  
Ricardo F. Martinez-Botas

Mobile organic Rankine cycle (MORC) systems represent a candidate technology for the reduction of fuel consumption and CO2 emissions from heavy-duty vehicles. Through the recovery of internal combustion engine waste heat, energy can be either compounded or used to power vehicle ancillary systems. Waste heat recovery systems have been shown to deliver fuel economy improvements of up to 13% in large diesel engines [1]. Whilst the majority of studies focus on individual component performance under specific thermodynamic conditions, there has been little investigation into the effects of expander specification across transient test cycles used for heavy-duty engine emission certification. It is this holistic approach which will allow prediction of the validity of MORC systems for different classes of heavy-duty vehicle, in addition to providing an indication of system performance. This paper first describes a meanline (one-dimensional simulation along a mean streamline within a flow passage) model for radial ORC turbines, divided into two main subroutines. An on-design code takes a thermodynamic input, before generating a candidate geometry for a chosen operating point. The efficacy of this design is then evaluated by an off-design code, which applies loss correlations to the proposed geometry to give a prediction of turbine performance. The meanline code is then executed inside a quasi-steady-state ORC cycle model, using reference emission test cycles to generate exhaust (heat source) boundary conditions, generated by a simulated 11.7L heavy-duty diesel engine. A detailed evaporator model, developed using the NTU-effectiveness method and single/two-phase flow correlations, provides accurate treatment of heat flow within the system. Together, these elements allow estimation of ORC system performance across entire reference emission test cycles. In order to investigate the limits of MORC performance, a Genetic Algorithm is applied to the ORC expander, aiming to optimize the geometry specification (radii, areas, blade heights, angles) to provide maximal time-averaged power output. This process is applied across the reference duty cycles, with the implications on power output and turbine geometry discussed for each. Due to the large possible variation in thermodynamic conditions within the turbine operating range a typical ideal-gas methodology (generating a single operating map for interpolation across all operating points) is no longer accurate — a complete off-design calculation must therefore be performed for all operating points. To reduce computational effort, discretization of the ORC thermodynamic inputs (temperature, mass flow rate) is investigated with several strategies proposed for reduced-order simulation. The paper concludes by predicting which heavy-duty emission test cycles stand to benefit the most from this optimization procedure, along with a comparison to existing transient results. Duty cycles containing narrow bands of operation were found to provide optimal performance, with a Constant-Speed, Variable-Load cycle achieving an average power output of 4.60 kW. Consideration is also given to the effectiveness of the methodology contained within the paper, the challenges of making ORC systems viable for mobile applications, along with suggestions for future research developments.

Author(s):  
Fredrik Ahlgren ◽  
Maria E. Mondejar ◽  
Magnus Genrup ◽  
Marcus Thern

Maritime transportation is a significant contributor to SOx, NOx and particle matter emissions, even though it has a quite low CO2 impact. New regulations are being enforced in special areas that limit the amount of emissions from the ships. This fact, together with the high fuel prices, is driving the marine industry towards the improvement of the energy efficiency of current ship engines and the reduction of their energy demand. Although more sophisticated and complex engine designs can improve significantly the efficiency of the energy systems in ships, waste heat recovery arises as the most influent technique for the reduction of the energy consumption. In this sense, it is estimated that around 50% of the total energy from the fuel consumed in a ship is wasted and rejected in fluid and exhaust gas streams. The primary heat sources for waste heat recovery are the engine exhaust and the engine coolant. In this work, we present a study on the integration of an organic Rankine cycle (ORC) in an existing ship, for the recovery of the main and auxiliary engines exhaust heat. Experimental data from the operating conditions of the engines on the M/S Birka Stockholm cruise ship were logged during a port-to-port cruise from Stockholm to Mariehamn over a period of time close to one month. The ship has four main engines Wärtsilä 5850 kW for propulsion, and four auxiliary engines 2760 kW used for electrical consumers. A number of six load conditions were identified depending on the vessel speed. The speed range from 12–14 knots was considered as the design condition, as it was present during more than 34% of the time. In this study, the average values of the engines exhaust temperatures and mass flow rates, for each load case, were used as inputs for a model of an ORC. The main parameters of the ORC, including working fluid and turbine configuration, were optimized based on the criteria of maximum net power output and compactness of the installation components. Results from the study showed that an ORC with internal regeneration using benzene would yield the greatest average net power output over the operating time. For this situation, the power production of the ORC would represent about 22% of the total electricity consumption on board. These data confirmed the ORC as a feasible and promising technology for the reduction of fuel consumption and CO2 emissions of existing ships.


2016 ◽  
Author(s):  
Roberto Cipollone ◽  
Davide Di Battista ◽  
Andrea Perosino ◽  
Federica Bettoja

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